Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies

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Bayesian Structure Adaptation for Continual Learning (2019, background): “Continual learning is also desirable in unsupervised learning problems as well (Smith et al., 2019; Rao et al., 2019) where the goal is to learn the underlying structure or latent representation of the data.”
Continual Unsupervised Representation Learning (2019, methodology): “In this paper, we propose to address the more general unsupervised continual learning setting (also suggested separately by Smith et al. (2019)), in which task labels and boundaries are not provided to the learner, and hence the focus is on unsupervised task learning.”
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2 (2022, methodology): “Our methods operate over handcrafted representations of the observation space; the system could be extended by introducing representation learning from RGB-observations via unsupervised (STAM (Smith et al., 2019a;b)) and self-supervised learning (Gallardo et al., 2021).”
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey (2022, methodology): “To expand the representations for novel classes and unlabeled data, several strategies are adopted to dynamically update representations in the latent space, such as creating new centroids by online clustering [236] and updating mixture-of-Gaussians [232].”